Deep Learning Toolbox Multiply by [] in Second Learning Iteration?

In the following code for a Conditional Variational Autoencoder I get the following error on the second iteration. I don't see any unusual values in the learnables for the encoderNet or decoderNet. It appears that this error doesn't appear when I run it in 2022a using the "run" button on this page.

Output in 2021a:

>> minimum_cvae

iteration =

1

loss =

1(C) × 1(B) single dlarray

6.6875

iteration =

2

loss =

1(C) × 1(B) single dlarray

6.6573

Error using .*

Arrays have incompatible sizes for this operation.

Error in tp26c2fafb_59b5_412b_af58_81dab1a42f5d (line 36)

tmp_33 = ((tmp_30.*[]).*constants{2});

Error in

deep.internal.recording.convert.tapeToFunction>@(varargin)fcnWithConstantsInput(varargin{:},constants)

(line 37)

fcn = @(varargin)fcnWithConstantsInput(varargin{:},constants);

Error in deep.internal.recording.CodegenExtensionMethod/backward (line

61)

[varargout{1:meth.NumGradients}] =

meth.BackwardFunction(varargin{:});

Error in deep.internal.dlarray.ExtensionOperation/backward (line 46)

[varargout{1:nargout}] = backward(op.Method, varargin{:});

Error in deep.internal.recording.RecordingArray/backwardPass (line 70)

grad = backwardTape(tm,{y},{initialAdjoint},x,retainData,false);

Error in dlarray/dlgradient (line 83)

[grad,isTracedGrad] = backwardPass(y,xc,pvpairs{:});

Error in minimum_cvae>modelGradients (line 64)

[decodeGrad, encodeGrad] = dlgradient(loss, decoderNet.Learnables, ...

Error in deep.internal.dlfeval (line 18)

[varargout{1:nout}] = fun(x{:});

Error in dlfeval (line 41)

[varargout{1:nout}] = deep.internal.dlfeval(fun,varargin{:});

Error in minimum_cvae (line 47)

[encodeGrad, decodeGrad] = dlfeval(...

Related documentation

Code:

%generate some fake data
XTrain=dlarray(randn(10,100),'CB');%Autoencoder inputs
YTrain=dlarray(randn(2,100),'CB');%Conditional inputs
N_input=size(XTrain,1);
N_latent = 9;%number of latent variables
N_cond=size(YTrain,1);%number of conditional inputs

%define encoder with two inputs
encoderLG = layerGraph([
    featureInputLayer(N_input,'Name','input_encoder');
    concatenationLayer(1,2,'Name','concat1')
    fullyConnectedLayer(5,'Name','fc1')
    reluLayer('Name','relu1')
    fullyConnectedLayer(2 * N_latent, 'Name', 'fc_encoder')
    ]);
encoderLG = addLayers(encoderLG,featureInputLayer(N_cond,'Name','cond_input'));
encoderLG = connectLayers(encoderLG,"cond_input","concat1/in2");

%definte decoder
decoderLG = layerGraph([
    featureInputLayer(N_latent,'Name','input_decoder');
    concatenationLayer(1,2,'Name','concat1')
    fullyConnectedLayer(5,'Name','fc1')
    reluLayer('Name','relu1')
    fullyConnectedLayer(N_input, 'Name', 'fc_decoder')
    ]);

decoderLG = addLayers(decoderLG,featureInputLayer(N_cond,'Name','cond_input'));
decoderLG = connectLayers(decoderLG,"cond_input","concat1/in2");

encoderNet = dlnetwork(encoderLG);
decoderNet = dlnetwork(decoderLG);

numEpochs = 10;
lr = 1e-3;
iteration=0;

avgGradientsEncoder = [];
avgGradientsSquaredEncoder = [];
avgGradientsDecoder = [];
avgGradientsSquaredDecoder = [];


for epoch = 1:numEpochs
    iteration=iteration+1
       
    [encodeGrad, decodeGrad] = dlfeval(...
        @modelGradients, encoderNet, decoderNet, XTrain, YTrain);
    
    [decoderNet.Learnables, avgGradientsDecoder, avgGradientsSquaredDecoder] = ...
        adamupdate(decoderNet.Learnables, ...
        decodeGrad, avgGradientsDecoder, avgGradientsSquaredDecoder, iteration, lr);
    [encoderNet.Learnables, avgGradientsEncoder, avgGradientsSquaredEncoder] = ...
        adamupdate(encoderNet.Learnables, ...
        encodeGrad, avgGradientsEncoder, avgGradientsSquaredEncoder, iteration, lr);
end

iteration = 1

loss = 
  1(C) × 1(B) single dlarray

    6.5279

iteration = 2

loss = 
  1(C) × 1(B) single dlarray

    6.5519

iteration = 3

loss = 
  1(C) × 1(B) single dlarray

    6.5803

iteration = 4

loss = 
  1(C) × 1(B) single dlarray

    6.5597

iteration = 5

loss = 
  1(C) × 1(B) single dlarray

    6.5038

iteration = 6

loss = 
  1(C) × 1(B) single dlarray

    6.5244

iteration = 7

loss = 
  1(C) × 1(B) single dlarray

    6.4807

iteration = 8

loss = 
  1(C) × 1(B) single dlarray

    6.5267

iteration = 9

loss = 
  1(C) × 1(B) single dlarray

    6.4935

iteration = 10

loss = 
  1(C) × 1(B) single dlarray

    6.4696

function [encodeGrad, decodeGrad] = modelGradients(encoderNet, decoderNet, x, y)
[z, zMean, zLogvar] = sampling(encoderNet, x, y);
xPred = sigmoid(forward(decoderNet, z, y));
loss = ELBOloss(x, xPred, zMean, zLogvar)

[decodeGrad, encodeGrad] = dlgradient(loss, decoderNet.Learnables, ...
    encoderNet.Learnables);
end

function elbo = ELBOloss(x, xPred, zMean, zLogvar)

squares = 0.5*(xPred-x).^2;
weight=10;
reconstructionLoss  = mean(squares(:));

KL = mean( -.5 * mean(1 + zLogvar - zMean.^2 - exp(zLogvar), 1));

elbo = weight*reconstructionLoss + KL;
end

function [zSampled, zMean, zLogvar] = sampling(encoderNet, x, y)
encoded = forward(encoderNet, x, y);
d = size(encoded,1)/2;
zMean = encoded(1:d,:);
zLogvar = encoded(1+d:end,:);

sz = size(zMean);
epsilon = randn(sz);
sigma = exp(.5 * zLogvar);
z = epsilon .* sigma + zMean;
zSampled = dlarray(z, 'CB');
end